MLOps / ML Platform Engineer
At a glance
Mid-level ML Platform & Ops role at SumerSports LLC. Remote, United States · full-time · no salary stated · we estimate $144,980–$225,625 across the United States.
SumerSports LLC has not published a salary for this role. The figure below is our estimate, not theirs.
aimlroles summary, based on the employer's posting.
What you'll do
- Run the systems that move data through model training, deployment and inference at high throughput
- Build repeatable training and evaluation workflows with scheduling, version control and tracked artifacts
- Manage compute clusters and tune GPU and CPU workloads to reduce waste and operating costs
- Keep production inference APIs responsive, with autoscaling, safe releases and rollback paths
- Track service reliability, latency, cost, model drift and data quality; automate deployments and support research teams
What you bring
- At least four years in ML platform, DevOps or infrastructure engineering
- Strong working knowledge of Kubernetes, containers, CI/CD and a major cloud platform
- You have managed GPU clusters and pipelines for model training or inference
- You can operate production ML systems against SLOs and build automation in Python and infrastructure as code
Who this fits
This mid-level role suits an engineer who can take machine-learning work from research into production and maintain the platform behind it. It is remote within the United States.
From the employer
SumerSports is a leading football intelligence technology company that specializes in providing an innovative suite of products for football fans and NFL clubs. We are a collection of executives, engineers, data scientists, and visionaries from NFL clubs, technology startups, finance, and academia.
Our data-driven platform empowers teams with insights and tools to make informed decisions within salary cap constraints. The platform also serves the NCAA, offering insights around the transfer portal and more.
What sets us apart is our unique blend of big tech talent, data scientists, and former NFL personnel, who have a combined 600+ years of NFL experience. Our domain knowledge is augmented by AI and machine learning technologies to create a unique view into many aspects of Football.
Responsibilities
- Design and operate ML infrastructure: Manage data, training, serving, and inference systems for high-throughput model workflows.
- Build scalable pipelines: Implement reproducible training and evaluation pipelines with versioning, scheduling, and artifact tracking.
- Optimize compute and cost: Tune GPU and CPU workloads, manage clusters, and drive efficiency via rightsizing, spot scheduling, and caching.
- Serve models in production: Operate APIs for low-latency inference with autoscaling, blue-green or canary rollouts, and rollback safety.
- Ensure reliability and observability: Define and own SLOs; instrument pipelines and services to track latency, cost, drift, and data quality.
- Secure and automate: Manage IAM, secrets, and container security; automate deployment pipelines via CI/CD and infrastructure as code.
- Collaborate cross-functionally: Partner with research scientists and AI engineers to deliver models from experiment to production with minimal friction.
- Document and enable: Build templates, runbooks, and internal tooling that make ML workflows repeatable, safe, and fast.
Qualifications
- 4+ years of experience in ML platform, DevOps, or infrastructure engineering.
- Deep knowledge of Kubernetes, CI/CD, containers, and cloud infrastructure (AWS, GCP, or Azure).
- Hands-on experience managing GPU clusters and training/inference pipelines.
- Familiarity with data orchestration and storage formats (Delta, Parquet, Polars, Spark).
- Proven ability to ship and operate production ML systems with SLOs.
- Strong Python skills and comfort with infrastructure as code and automation.
- Experience with observability and cost optimization at scale.
Nice to Have
- Experience with real-time or low-latency model serving (REST, gRPC).
- Exposure to model registry and promotion workflows.
- Familiarity with data quality, lineage, and curation pipelines.
- Background in sports analytics or other high-volume data domains.
- Experience integrating LLM workflows or evaluation pipelines.
Benefits
- Competitive Salary and Bonus Plan
- Comprehensive health insurance plan
- Retirement savings plan (401k) with company match
- Remote working environment
- A flexible, unlimited time off policy
- Generous paid holiday schedule - 13 in total including Monday after the Super Bowl
SumerSports is committed to fair and equitable compensation practices.
Actual compensation packages are based on several factors that are unique to each candidate, including but not limited to skill set, depth of experience, certifications, and specific work location. This may be different in other locations due to differences in the cost of labor.
The total compensation package for this position may also include annual performance bonus, benefits and/or other applicable incentive compensation plans.